code: def test_1m_2cf_metric_dict():
      target = MetricFrame(
          metrics={"recall_score": skm.recall_score},
          y_true=y_t,
          y_pred=y_p,
          sensitive_features=g_3,
          control_features=np.stack((g_1, g_2), axis=1),
      )
      assert target._user_supplied_callable is False
  
      assert isinstance(target.overall, pd.DataFrame)
      assert target.overall.shape == (4, 1)
      assert np.array_equal(
          target.overall.index.names,
          ["control_feature_0", "control_feature_1"],
      )
      mask_a_f = np.logical_and((g_1 == "aa"), (g_2 == "f"))
      mask_a_g = np.logical_and((g_1 == "aa"), (g_2 == "g"))
      mask_b_f = np.logical_and((g_1 == "ba"), (g_2 == "f"))
      mask_b_g = np.logical_and((g_1 == "ba"), (g_2 == "g"))
  
      exp_a_f = skm.recall_score(y_t[mask_a_f], y_p[mask_a_f])
      exp_a_g = skm.recall_score(y_t[mask_a_g], y_p[mask_a_g])
      exp_b_f = skm.recall_score(y_t[mask_b_f], y_p[mask_b_f])
      exp_b_g = skm.recall_score(y_t[mask_b_g], y_p[mask_b_g])
  
      assert target.overall["recall_score"][("aa", "f")] == exp_a_f
      assert target.overall["recall_score"][("aa", "g")] == exp_a_g
      assert target.overall["recall_score"][("ba", "f")] == exp_b_f
      assert target.overall["recall_score"][("ba", "g")] == exp_b_g
  
file_path: fairlearn/test/unit/metrics/test_metricframe_overall.py
imports: import numpy
  import pandas
  import pytest
  import sklearn.metrics
  from fairlearn.metrics import MetricFrame
  from .data_for_test import g_1, g_2, g_3, s_w, y_p, y_t
label_desc: Use image node_id 11 for calling a global function with example usage: test_1m_2cf_metric_dict() without return types